fbrundu

#%%
# Compute posterior of theta from coin tosses

#%%
%config InlineBackend.figure_format = 'retina'

#%%
from matplotlib import pyplot as plt
import numpy as np
import seaborn as sns

fbrundu

alias R="<path_to_R>/R-X.Y.Z/bin/R"
export R_LIBS="<path_to_R>/packages"
export PATH="<path_to_R>/R-X.Y.Z/bin:${PATH}"

fbrundu

# -*- coding: utf-8 -*-

import logging as log
import pandas as pd
import requests as rq


class TCGA:

  def __init__(self, gdc_url='https://gdc-api.nci.nih.gov', per_page=100,

fbrundu

<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
	<key>ANSIBlackColor</key>
	<data>
	YnBsaXN0MDDUAQIDBAUGFRZYJHZlcnNpb25YJG9iamVjdHNZJGFyY2hpdmVyVCR0b3AS
	AAGGoKMHCA9VJG51bGzTCQoLDA0OVU5TUkdCXE5TQ29sb3JTcGFjZVYkY2xhc3NPECcw
	LjExNzY0NzA1ODggMC4xMjk0MTE3NjQ3IDAuMTUyOTQxMTc2NQAQAYAC0hAREhNaJGNs
	YXNzbmFtZVgkY2xhc3Nlc1dOU0NvbG9yohIUWE5TT2JqZWN0XxAPTlNLZXllZEFyY2hp

fbrundu

.CodeMirror, div.prompt.input_prompt, div.prompt.output_prompt, pre {
    font-family: "Inconsolata for Powerline";
    font-size: 100%;
}

fbrundu

<style>

html {
  font-size: 62.5% !important; }
body {
  font-size: 1.5em !important; /* currently ems cause chrome bug misinterpreting rems on body element */
  line-height: 1.6 !important;
  font-weight: 400 !important;
  font-family: "HelveticaNeue", "Helvetica Neue", Helvetica, Arial, sans-serif !important;
  color: #222 !important; }

fbrundu

import pandas as pd
import sys
import re

tcga_tsv = sys.argv[1]

tcga = pd.read_table(tcga_tsv, sep='\t', index_col=0)

oldcolumns = tcga.columns.tolist()
newcolumns = ['-'.join(re.findall(r'TCGA[^_]*', oc)[0].split('-')[:4])

fbrundu

import pandas as pd
import numpy as np
import sys
import random as rnd

csv = sys.argv[1]
out = sys.argv[2]

df = pd.read_table(csv, sep='\t', index_col=0)

fbrundu

import pandas as pd
import sys
import glob
import os

# input / output directory
input_dir = sys.argv[1]
# input file extension
input_ext = sys.argv[2]
# cardinality of index columns (rownames)

fbrundu

If you bet on "red" at roulette, you have chance 18/38 of winning. Suppose you make a sequence of independent bets on “red” at roulette, with the decision that you will stop playing once you have won 5 times. What is the chance that after 15 bets you are still playing?

We use [binomial][1] probability mass function. To calculate the probability, you have to estimate the probability of having up to 4 successful bets after the 15th. So the final probability will be the sum of the probability to get 0 successful bets in 15 bets, plus the probability to get 1 successful bet, ..., to the probability of having 4 successful bets in 15 bets.

To achieve it:

import scipy.stats as ss

n = 15         # Number of total bets

p = 18./38 # Probability of getting "red" at the roulette